A method for zoned management of groundwater resources

Abstract

The application discloses a groundwater source area partition management method, and belongs to the technical field of groundwater resource management and water source area protection; multi-source data is unified to a spatial grid, a human activity pressure, an improved DRASTIC protection performance, a Fi-Nemerow water quality evaluation, a protected area control intensity and a characteristic pollutant dynamic increment factor system are constructed; an adaptive weight is formed by fusing an entropy weight and an expert weight, basic management criteria are calculated, and consistency constraint is performed on pollution risk; Monte Carlo simulation is adopted to quantize uncertainty and partition confidence, a stability criterion is set to identify a review area; under budget constraint, a monitoring point is optimized to maximize information gain, and low-cost dynamic updating is realized. The application can effectively solve the problems of weight solidification, risk disconnection, partition instability and high maintenance cost of a traditional method, significantly improves the reliability, stability and economy of water source area partition, and is suitable for hierarchical control and long-term management of groundwater source areas such as pore water and phreatic water.

Description

Technical Field

[0001] This invention relates to the field of groundwater resource management and source water protection technology, and in particular to a method for zoning management of groundwater source areas. Background Technology

[0002] In engineering practice, the zoning management of groundwater source areas requires long-term, stable, and executable zoning levels and measures. However, the lack of monitoring data, the complexity of actual hydrogeological conditions, and the high uncertainty of model parameters lead to inaccurate zoning results and poor zoning stability. Improving zoning accuracy requires a large investment.

[0003] In pore water-type and riverside water sources, pollutant migration is jointly controlled by the groundwater level-exploitation funnel, river-groundwater exchange, and the heterogeneity of the aquifer medium. This leads to changes in the pollution contribution and management priority of the same area at different times. However, in reality, the number of monitoring wells is limited, the monitoring frequency is constrained by cost, and the sources of water quality data, geological parameters, land use, and pollution source information are diverse and at different scales. When zoning methods cannot handle this heterogeneity and uncertainty, the resulting zoning maps cannot scientifically and effectively guide management work.

[0004] Common practices in existing technologies include: dividing control zones based on water quality level or single risk assessment results; or using vulnerability indices (such as DRASTIC) and weighted superposition with water quality and pollution sources to obtain zoning results. These methods have the following substantial defects in practical applications: (1) fixed weights and subjective biases are difficult to avoid, resulting in poor cross-regional portability; (2) lack of a risk-management consistency mechanism, either "risk is too dominant" or "risk is ignored"; (3) uncertainty is not quantified, resulting in frequent changes in zoning levels with data updates, making management unenforceable; (4) lack of a mechanism to achieve updates with "minimum cost", resulting in high zoning maintenance costs. Based on the obvious technical shortcomings in the above four aspects, a groundwater source zoning management method that can simultaneously meet the requirements of reliability, stability and economy is needed. Summary of the Invention

[0005] This invention provides a method for zoning management of groundwater source areas to solve the problems of fixed weights, lack of risk constraints, unquantified uncertainty, and high update costs in existing technologies, thereby achieving more accurate, stable, and economical zoning results.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a method for zoned management of groundwater source areas, comprising:

[0008] S1: Acquire multi-source data of the study area, project them uniformly to the same coordinate system and map them to a unified spatial grid; S2: Construct a multi-factor system including human activity pressure factor, aquifer protection performance factor, groundwater quality comprehensive evaluation factor, protected area control intensity factor, and characteristic pollutant dynamic increment factor; S3: Calculate and construct the protected area control intensity factor based on the empirical formula of protected area radius.

[0009] S4: The improved DRASTIC model, which removes the topographic slope and increases the aquifer thickness, is used to calculate the aquifer protection performance factor;

[0010] S5: The comprehensive evaluation factor of groundwater quality is calculated by combining the Fi score and the Nemerow composite index;

[0011] S6: Calculate the human activity pressure factor based on land use type assignment;

[0012] S7: Calculate the dynamic increment factor of characteristic pollutants by the difference in pollutant concentration under different operating conditions;

[0013] S8: Standardize and robustly handle quantile truncation anomalies for all factors in the multi-factor system;

[0014] S9: Integrate entropy weights and expert weights to construct an integrated weight that adapts to data completeness;

[0015] S10: Basic management criteria based on multi-factor and adaptive weight calculation;

[0016] S11: Use pollution risk results as a consistency constraint to perform risk correction on basic management criteria; S12: Use Monte Carlo simulation to propagate uncertainty and calculate the confidence level of each grid partition.

[0017] S13: Set stability criteria based on confidence threshold, identify and extract partition verification areas;

[0018] S14: Under budget constraints, with the goal of maximizing information gain, optimize the deployment of new monitoring points to achieve low-cost dynamic updates of zones.

[0019] Furthermore, the formula for calculating the radius of the protected area in step S3 is: R=(α・K・I・T) / n, where α is the safety factor, K is the permeability coefficient, I is the hydraulic gradient, T is the migration time, and n is the effective porosity.

[0020] Furthermore, the adaptive fusion weights in step S9 are:

[0021] in, For the first The fusion weights of the factors; For the first The objective weights of each factor; For the first The management preference weights of each factor; This refers to the objective weighting ratio coefficient. Weighting of management preferences; For factor index;

[0022] Adaptive data completeness:

[0023]

[0024] in,

[0025] Effective monitoring information volume, expressed as the number of monitoring points, the number of effective samples, or the information coverage rate;

[0026] Smoothing constant.

[0027] Furthermore, the risk correction formula in step S11 is:

[0028] in:

[0029] Correction strength coefficient;

[0030] : Monotonic mapping.

[0031] Furthermore, the rules for determining the review area in step S13 are as follows: Then the grid will be Included in the review process;

[0032] in, This represents the confidence threshold region.

[0033] Furthermore, in step S14, with the goal of maximizing uncertainty reduction, new monitoring points are selected under budget constraints.

[0034] Compared with the prior art, the technical solution disclosed in this invention has the following beneficial effects:

[0035] Step S9 integrates entropy weights and expert weights to achieve adaptive adjustment of data completeness, avoiding fixed weights and subjective biases, and improving the method's cross-regional applicability. Step S11 uses pollution risk as a consistency constraint to correct management criteria, matching zoning results with risk levels and improving the scientific rigor of zoning. Steps S12–S13 employ Monte Carlo quantification of confidence, stability criteria, and a verification zone mechanism to suppress frequent changes in zoning levels, significantly improving management feasibility. Step S14 optimizes monitoring points by maximizing information gain under budget constraints, improving zoning accuracy at the lowest cost and significantly reducing long-term management investment. This addresses the problems of fixed weights, lack of risk constraints, unquantified uncertainty, and high update costs in existing technologies, resulting in more accurate, stable, and economical zoning results. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A schematic diagram of the protected area provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the vulnerability space results and partitioning provided in the embodiments of the present invention;

[0039] Figure 3 This is a schematic diagram of the spatial distribution of water quality levels provided in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the spatial distribution of land use types provided in an embodiment of the present invention;

[0041] Figure 5 This invention provides a schematic diagram of ammonia nitrogen distribution in water sources before and after river pollution. Figure 1 ;

[0042] Figure 6 This invention provides a schematic diagram of ammonia nitrogen distribution in water sources before and after river pollution. Figure 2 ;

[0043] Figure 7 This is a schematic diagram of risk zoning results provided in an embodiment of the present invention;

[0044] Figure 8 This is a schematic diagram of the research area control zone division results provided in an embodiment of the present invention;

[0045] Figure 9 A schematic diagram of the groundwater source area zoning management method provided in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] This invention provides a method for zoning management of groundwater source areas, which can solve the problems of fixed weights, lack of risk constraints, unquantified uncertainty, and high update costs in existing technologies, and achieve more accurate, stable and economical zoning results.

[0049] like Figures 1-9 As shown, this embodiment of the invention provides a method for zoning management of groundwater source areas, including:

[0050] S1: Acquire multi-source data of the study area, project them uniformly to the same coordinate system and map them to a unified spatial grid; S2: Construct a multi-factor system including human activity pressure factor, aquifer protection performance factor, groundwater quality comprehensive evaluation factor, protected area control intensity factor, and characteristic pollutant dynamic increment factor; S3: Calculate and construct the protected area control intensity factor based on the empirical formula of protected area radius.

[0051] The formula for calculating the radius of the protected area in step S3 is: R=(α・K・I・T) / n, where α is the safety factor, K is the permeability coefficient, I is the hydraulic gradient, T is the migration time, and n is the effective porosity.

[0052] S4: The improved DRASTIC model, which removes the topographic slope and increases the aquifer thickness, is used to calculate the aquifer protection performance factor;

[0053] S5: The comprehensive evaluation factor of groundwater quality is calculated by combining the Fi score and the Nemerow composite index;

[0054] S6: Calculate the human activity pressure factor based on land use type assignment;

[0055] S7: Calculate the dynamic increment factor of characteristic pollutants by the difference in pollutant concentration under different operating conditions;

[0056] S8: Standardize and robustly handle quantile truncation anomalies for all factors in the multi-factor system;

[0057] S9: Integrate entropy weights and expert weights to construct an integrated weight that adapts to data completeness;

[0058] The adaptive fusion weights in step S9 are:

[0059] in, For the first The fusion weights of the factors; For the first The objective weights of each factor; For the first The management preference weights of each factor; This refers to the objective weighting ratio coefficient. Weighting of management preferences; For factor index;

[0060] Adaptive data completeness:

[0061]

[0062] in,

[0063] Effective monitoring information volume, expressed as the number of monitoring points, the number of effective samples, or the information coverage rate;

[0064] Smoothing constant.

[0065] S10: Basic management criteria based on multi-factor and adaptive weight calculation;

[0066] S11: Use pollution risk results as a consistency constraint to perform risk correction on basic management criteria;

[0067] The risk correction formula in step S11 is:

[0068] in:

[0069] For the first Corrected management criteria for each grid cell; For the first Basic management criteria for each grid unit; For the first Pollution risk value of each grid cell; To correct the strength coefficient; It is a monotonic mapping function; Number the grid cells.

[0070] S12: Uncertainty propagation is performed using Monte Carlo simulation to calculate the confidence level of each grid partition;

[0071] S13: Set stability criteria based on confidence threshold, identify and extract partition verification areas;

[0072] The rules for determining the review area in step S13 are as follows: Then the grid will be Included in the review process;

[0073] in, For the first The partition management level of each grid cell obtained in this calculation; For the first The previous zoning management level of each grid unit; For the first Partition confidence of each grid cell; The confidence threshold; Number the grid cells.

[0074] S14: Under budget constraints, with the goal of maximizing information gain, optimize the deployment of new monitoring points to achieve low-cost dynamic updates of zones.

[0075] In step S14, with the goal of maximizing uncertainty reduction, new monitoring points are selected under budget constraints.

[0076] The above steps are explained in detail below:

[0077] S1: Data Acquisition and Spatial Unification

[0078] Acquire at least the following data layers: land use, groundwater quality monitoring data, hydrogeological parameters (depth, recharge, medium, permeability coefficient, porosity, aquifer thickness, etc.), water source protection area boundaries / parameters, and distribution or simulation results of characteristic pollutants under different operating conditions (such as river pollution participation / non-participation, different mining intensities).

[0079] Project all data onto a unified coordinate system, and map them to a rasterized or interpolated system. Above, ensure that each grid cell has a computable factor input.

[0080] Output: Raw factor data on a uniform grid .

[0081] S2: Factor System Construction and Direction Definition

[0082] Construct a factor set (example, containing at least the following five categories):

[0083] Human activity stress factor (positive: the higher the value, the greater the stress).

[0084] Protection performance / vulnerability factor (positive: the more "vulnerable", the greater the vulnerability).

[0085] Water quality comprehensive evaluation factor (positive: the worse the water quality, the greater the factor).

[0086] : Control intensity factor for protected areas (positive: the stricter the control / the more core the area, the larger the value assigned)

[0087] : Dynamic factors of characteristic pollutants (positive: the larger the dynamic increment, the more management needs to be strengthened)

[0088] Note: Defining the direction is the foundation for subsequent standardization and coupling, avoiding directional confusion such as "the better the water quality, the larger the value" that could lead to reversed results.

[0089] S3: Calculation of protected area attribute factors

[0090] For pore water-type unconfined water sources, the radius of the protection zone is determined using empirical methods:

[0091] in,

[0092] Radius of the protected area (m)

[0093] Safety factor (%), used to account for future changes in inflow and radius expansion due to drought effects.

[0094] Aquifer permeability coefficient (m / d)

[0095] Hydraulic gradient (average hydraulic gradient within the funnel area)

[0096] : Horizontal migration time of pollutants (d)

[0097] Effective porosity (dimensionless)

[0098] Based on the radii and boundaries of primary and secondary protected areas, the protected area zones are determined, and the control intensity factors of the protected areas are constructed:

[0099] in, These are configurable parameters (e.g., 0.6, 0.3), set by the management department according to the protection objectives.

[0100] Output: .

[0101] S4: Protection Performance / Vulnerability Factor Calculation

[0102] The improved model is obtained by removing topographic slope (T) and increasing aquifer thickness (T) from the original DRASTIC factor. The improved DRASTIC index is:

[0103] in,

[0104] These include: groundwater depth, net recharge, aquifer medium, soil medium, influence of vadose zone medium, hydraulic conductivity, and aquifer thickness.

[0105] Subscript Weight; Subscript :score

[0106] The larger the value, the more fragile it is.

[0107] Weighting: For the newly added aquifer thickness index, we consulted experts, removed extreme values, took the average value, and then integrated it with the original weighting system.

[0108] Constructing vulnerability factors:

[0109]

[0110] Output: .

[0111] S5: Calculation of Comprehensive Water Quality Evaluation Factors

[0112] The comprehensive evaluation process using GB / T14848 is as follows: First, determine the categories based on single factors. Then calculate the overall score. .

[0113] (1) Assigning values ​​to individual components: assigning values ​​to each indicator In the grid concentration Based on standard category mapping to individual scores .

[0114] (2) Overall score: Let If the number of indicators involved in the evaluation is:

[0115] Overall rating:

[0116] in,

[0117] The average score for each item reflects the overall level.

[0118] The worst single-item score reflects the "weakest link effect".

[0119] Overall score, taking into account both average performance and worst performance.

[0120] Constructing water quality factors:

[0121]

[0122] Output: .

[0123] S6: Calculation of Human Activity Stress Factors

[0124] For each grid According to its land use type Assignment:

[0125] Output: .

[0126] S7: Calculation of Dynamic Factors of Characteristic Pollutants

[0127] Considering the distribution of ammonia nitrogen before and after river pollution, a dynamic incremental factor is constructed:

[0128] in,

[0129] Considering river pollution involvement (or concentration under certain operating conditions)

[0130] Concentration without considering river pollution involvement (or baseline conditions)

[0131] Measuring the "amplification effect" of changes in external operating conditions on the risk of water sources is a key input that distinguishes management zoning from static evaluation.

[0132] Output: .

[0133] S8: Factor Standardization and Robustness

[0134] Unify all factors to :

[0135] Positive factors:

[0136]

[0137] Negative factor:

[0138]

[0139] To suppress outliers, quantile truncation robustness is employed:

[0140] in, For the first On the _ grid cell _ Standardized values ​​of each factor; For the first On the _ grid cell _ The original values ​​of each factor; and The first The maximum and minimum values ​​of each factor across all grid cells; For the first On the _ grid cell The values ​​of the factors after quantile truncation; and The first Each factor sample in and quantiles at; This is the quantile truncation ratio parameter; For factor index; Number the grid cells.

[0141] Output: Standardization factor .

[0142] S9: Adaptive Weights

[0143] (1) Objective entropy weight

[0144] in, For the first The factor in the th Normalized percentage on each grid cell; For the first On the _ grid cell Standardized values ​​of each factor; For the first Information entropy of each factor; For the first Objective entropy weight of each factor; This refers to the number of grid cells; The number of factors; For factor index; The grid cell number; Indicates the first Each grid cell.

[0145] (2) Management preference weight

[0146] Obtained from pairwise comparison matrices and pass the consistency test (e.g. This ensures the reliability of the judgment.

[0147] (3) Fusion weights

[0148]

[0149] in, Weights for management preferences; For objective weighting percentage

[0150] Adaptive data completeness:

[0151]

[0152] in,

[0153] Effective monitoring information volume (which can be expressed as the number of monitoring points, the number of valid samples, or the information coverage rate)

[0154] Smoothing constant, used to avoid Extreme values ​​(in engineering, these can be on the order of 10–50, depending on the size of the region)

[0155] Output: Fusion Weights .

[0156] S10: Basic Management Criteria

[0157]

[0158] in, Basic management criteria (dimensionless); For weights (dimensionless); These are standardized factor values ​​(dimensionless).

[0159] Output: Basic criteria .

[0160] S11: Risk Constraint Correction

[0161] Constructing risk values Subsequently, it is used as a consistency constraint to correct the management criterion:

[0162]

[0163] in:

[0164] For the first Corrected management criteria for each grid cell; For the first Basic management criteria for each grid unit; For the first Pollution risk value of each grid cell; To correct the strength coefficient; It is a monotonic mapping function; Number the grid cells.

[0165] Output: Correction Criterion .

[0166] S12: Uncertainty Propagation and Confidence Calculation

[0167] Considering that the errors mainly originate from water quality monitoring errors, spatial interpolation errors, weighting errors, and risk model parameter errors, Monte Carlo propagation is adopted:

[0168]

[0169] Mean:

[0170]

[0171] Standard deviation:

[0172]

[0173] Confidence level:

[0174]

[0175] in,

[0176] Number of samples

[0177] , , : in the Perturbation parameters / input under the second sampling

[0178] Criterion Mean

[0179] Criterion Discreteness

[0180] Normalized benchmark

[0181] Confidence level

[0182] Output: .

[0183] S13: Stability Criterion and Verification Region Extraction

[0184] Candidate Level Compared with the previous period's level If changes occur and the confidence level is insufficient, the old level is maintained and the application proceeds to the review area:

[0185] ,but

[0186] in, For the first The partition management level of each grid cell obtained in this calculation; For the first The previous zoning management level of each grid unit; For the first Partition confidence of each grid cell; The confidence threshold; Number the grid cells. This is the set of areas for review.

[0187] S14: Minimum Cost Update Sampling Optimization

[0188] Construct a set of candidate new monitoring points For each candidate point Calculate its expected reduction in overall uncertainty:

[0189]

[0190] in, It can be selected as a global uncertainty measure, for example:

[0191]

[0192] Under budget constraints Find the optimal set of newly added points :

[0193]

[0194]

[0195] in,

[0196] Candidate point set

[0197] Optimal set of new points

[0198] Location Costs

[0199] Total Budget

[0200] Join point The resulting reduction in uncertainty.

[0201] The beneficial effects are as follows:

[0202] Quantitative indicators of cost reduction

[0203] The monitoring cost is defined as the combination of the cost of adding new monitoring points and the cost of operation and maintenance.

[0204]

[0205] in,

[0206] Number of monitoring points

[0207] Single well construction / deployment cost

[0208] Monitoring frequency

[0209] Cost per sample test

[0210] Cost reduction rate:

[0211]

[0212] in,

[0213] Unit cost of monitoring per operation

[0214] Total cost of the benchmark solution (comparison solution)

[0215] Total cost after using the method of this invention

[0216] Cost reduction rate (dimensionless) represents the percentage decrease in cost relative to the baseline solution. This indicates a reduction in costs.

[0217] Partition stability quantification index

[0218] Define the two-period zone change rate:

[0219]

[0220] stability:

[0221] in,

[0222] Spatial evaluation unit

[0223] : A collection of spatial units within the study / evaluation area

[0224] :gather Number of spatial units

[0225] Spatial unit Partition management level / category in this (updated) period

[0226] Spatial unit In the previous (before update) partition management level / category

[0227] Indicator function, takes the value 1 if the condition within the parentheses is true, otherwise takes the value 0.

[0228] Two-period zoning change rate (dimensionless), representing the proportion of units within the study area that "experienced a change in rank".

[0229] : Partition stability index (dimensionless), according to By definition, a larger value indicates greater stability.

[0230] Partition Confidence Measurement

[0231] Global average confidence level:

[0232]

[0233] Percentage of the area to be reviewed:

[0234]

[0235] in,

[0236] Spatial unit The partition confidence level (dimensionless) typically takes values ​​in the range of 1000-1000. The larger the value, the more reliable the partitioning conclusion of that unit.

[0237] Average confidence level across the entire domain (dimensionless)

[0238] : Set of review area units

[0239] Number of review units

[0240] Percentage of the review area (dimensionless), according to By definition, a smaller value indicates fewer uncertain regions and more certain partitioning.

[0241] The specific implementation method is as follows:

[0242] Taking a groundwater source as the research object, based on the existing land use data, hydrogeological parameters, water quality monitoring results, pollution risk assessment results, and protected area delineation results, the complete calculation process is implemented according to the multi-factor coupling and uncertainty constraint zoning management method described in the claims. The process gradually completes factor construction, standardization processing, weight fusion, risk constraint correction, uncertainty propagation analysis, zoning level division, and quantitative evaluation of management input, forming a zoning result map and statistical results that can be directly used for management decision-making.

[0243] This embodiment verifies that, under existing data conditions, the method of the present invention can achieve stable output of groundwater source area zoning management results, and significantly reduce management input while ensuring zoning confidence, demonstrating the technical effectiveness of the present invention in terms of engineering feasibility and economic rationality.

[0244] S1 Raw Data Input and Preprocessing

[0245] enter:

[0246] (1) Boundary of the study area and location of the water source: the vector boundary of the study area and the location of the water intake well / water intake structure.

[0247] (2) Original land use data: land use status vector / raster.

[0248] (3) Basic hydrogeological parameters: aquifer type, permeability coefficient Effective porosity Factors such as groundwater depth, net recharge, aquifer medium, soil medium, vadose zone medium, hydraulic conductivity, and aquifer thickness.

[0249] (4) Raw data of groundwater quality monitoring: coordinates of monitoring well locations, sampling time, and concentrations of various water quality indicators (used for the calculation of Fi and F).

[0250] (5) Operating conditions / scenario data: such as boundary conditions or simulation / observation results of “river pollution participation / non-participation” (used for dynamic differential analysis of characteristic pollutants).

[0251] deal with:

[0252] (1) Coordinate system 1. Spatial clipping to the study area;

[0253] (2) Data missing and outlier detection;

[0254] (3) All types of data are unified into the same computing grid / cell.

[0255] Output:

[0256] Output S1-1: Unified spatial computational unit (mesh / surface unit) attribute table (internal data layer);

[0257] Output S1-2: Five types of basic data layers (internal data layers) that can be used for subsequent calculations.

[0258] S2 Factor System Definition and Direction Unification

[0259] deal with:

[0260] In each spatial unit The above defines five categories of factors (with the unified direction of "the larger the value, the higher the management intensity requirement"):

[0261] Human activity stress factors;

[0262] Vulnerability factors;

[0263] Water quality comprehensive evaluation factors;

[0264] : Control intensity factor of the protected area;

[0265] : Dynamic pollution influencing factors.

[0266] Output:

[0267] Output S2-1: Factor dictionary and computation direction (internal data layer).

[0268] S3 Protected Area Calculation and Control Intensity Factor Construction

[0269] deal with:

[0270] Calculate the radius of the protected area using empirical methods:

[0271]

[0272] Based on this, the boundaries of primary and secondary protection zones are established; then, control intensity factors are constructed:

[0273]

[0274] Output:

[0275] Output S3-1: Range of Level I / Level II Protection Zone;

[0276] Output S3-2: Protected area parameters and radius results (internal data layer).

[0277] S4 Improves DRASTIC Computational Vulnerability

[0278] deal with:

[0279] Adopting an improved DRASTIC method (reducing slope and increasing aquifer thickness):

[0280]

[0281] definition .

[0282] Output:

[0283] Output S4-1: Indicator scoring / weighting basis (internal data layer);

[0284] Output S4-2: Vulnerability space results and partitioning;

[0285] Output S4-3: Vulnerability area statistics (internal data layer).

[0286] S5 Water Quality Fi–F Calculation

[0287] deal with:

[0288] Obtained by single indicator category And calculate:

[0289]

[0290] definition .

[0291] Output:

[0292] Output S5-1: Basis for Fi and F classification (internal data layer);

[0293] Output S5-2: Spatial results of water quality;

[0294] Output S5-3: Water area statistics (internal data layer).

[0295] S6 Land Use Pressure Calculation

[0296] deal with:

[0297] Based on land use type scoring, for each unit:

[0298]

[0299] Output:

[0300] Output S6-1: Spatial distribution of land use;

[0301] Output S6-2: Land use type score (internal data layer).

[0302] S7 Calculation of Dynamic Pollution Impact Factors

[0303] deal with:

[0304] Taking ammonia nitrogen as an example, the calculation is as follows:

[0305]

[0306] Output:

[0307] Output S7-1: Spatial representation of the difference in ammonia nitrogen before and after river involvement.

[0308] S8 Factor Standardization and Robustness

[0309] deal with:

[0310]

[0311] And perform quantile truncation robustness.

[0312] Output:

[0313] Output S8-1: Standardized Factor Data Layer (Internal data layer).

[0314] S9 Weight Adaptive Fusion

[0315] deal with:

[0316] Integration of Entropy Weights and Management Preferences:

[0317]

[0318] Pick And a set of fusion weights that can be directly used for reproduction is given:

[0319]

[0320] correspond .

[0321] Output:

[0322] Output S9-1: Fusion weight parameter table (internal data layer).

[0323] S10 Multi-factor Coupling Basic Criterion Calculation

[0324] deal with:

[0325]

[0326] Output:

[0327] Output S10-1: Basic Criteria Data layer (internal data layer).

[0328] S11 Risk Constraint Correction

[0329] deal with:

[0330] The risk assessment system and weights are used to form a risk outcome representation and risk space presentation.

[0331] Will Mapping and Correction:

[0332]

[0333] Output:

[0334] Output S11-1: Risk assessment system and weights (internal data layer);

[0335] Output S11-2: Risk Outcome Characterization and Classification (Internal Data Layer);

[0336] Output S11-3: Risk space results.

[0337] S12 Uncertainty Propagation and Confidence

[0338] deal with:

[0339] Monte Carlo spread :

[0340] Global average confidence level:

[0341] Output:

[0342] Output S12-1:

[0343] Output S12-2: Confidence Data Layer (Internal Data Layer)

[0344] S13 Partitioning and Final Partitioning Results Output

[0345] deal with:

[0346] right The final zone management level is obtained by dividing the zone into 0-4 levels. .

[0347] Output:

[0348] Output S13-1: Final partition management hierarchy diagram;

[0349] Output S13-2: Partition level feature description;

[0350] Output S13-3: Area statistics at the zoning level.

[0351] Control Zone Zoning Level Characteristic Description Table

[0352] Control zone level feature Level 0 control area The groundwater has low vulnerability (high pollution resistance), no pollution sources are distributed on the surface, and the risk of groundwater pollution is low; the groundwater quality meets Class I, II, and III standards. Level 1 Control Area Groundwater has relatively low vulnerability (high pollution resistance), but there are sporadic pollution sources on the surface, posing a certain risk of groundwater pollution; the groundwater quality is between Class III and IV. Level II Control Area Groundwater vulnerability is moderate (contamination resistance is moderate), some pollution sources are distributed on the surface, and the risk of groundwater contamination is moderate; groundwater quality is between Class IV and Class V. Level 3 Control Zone Groundwater is highly vulnerable (poor pollution resistance), with numerous pollution sources distributed on the surface, posing a high risk of groundwater contamination; groundwater quality is classified as Class V. Level IV Control Zone Groundwater is highly vulnerable (poor pollution resistance), and there are numerous pollution sources on the surface, posing a high risk of groundwater contamination; the groundwater quality is classified as Class V.

[0353] Control Zone Zoning Level Area Statistics Table

[0354] Control zone level Range of values <![CDATA[Area (km 2 )]]> Area percentage (%) Level 0 control area <0.5 89.95 14.19 Level 1 Control Area [0.5-0.58) 173.16 27.31 Level II Control Area [0.58-0.66) 159.67 25.18 Level 3 Control Zone [0.66-0.74) 161.89 25.53 Level IV Control Zone ≥0.74 49.33 7.78

[0355] S14 Update Processing Based on Confidence Constraints and Input Optimization (Simplified Version)

[0356] deal with:

[0357] Based on the confidence distribution obtained in step S12 Set confidence threshold , will satisfy Spatial units are designated as verification areas Within the review area, priority should be given to selecting areas located at the boundary of the zoning level or those governed by management criteria. Spatial units approaching the grading threshold are considered candidate areas for new monitoring points, provided that the number of new monitoring points does not exceed [a certain threshold]. Under the constraints, the locations of new monitoring points are determined sequentially from low to high confidence levels; simultaneously, based on the area of ​​each level of partition obtained in step S13... The differential management input and the unified high-level management input are calculated according to the definition of the "tiered input coefficient method" of this invention, thereby obtaining the input reduction rate. .

[0358] Output:

[0359] Output the scope of the review area and the layout plan of newly added monitoring points, and output the input comparison results and input reduction rate; the calculation results of this embodiment show that: the input for differentiated management by region is 2119.37 (relative unit), and the input for unified management of the whole region at the highest level is 5072.00 (relative unit), and the input reduction rate is... The confidence level was 58.21%. Additionally, the confidence level was recalculated after adding eight new monitoring points. The overall average confidence level increased from 0.79 to approximately 0.86, and the proportion of the review area decreased from 9.6% to approximately 4%, thereby improving the reliability of the zoning results and reducing the overall management investment while controlling the cost of new monitoring.

[0360] Terminology and Symbol Conventions

[0361] The study area was divided into a unified spatial grid set:

[0362]

[0363] in, Indicates the first One grid cell, This represents the number of grid cells.

[0364] Factor set:

[0365] Indicates the first Each factor in the grid The value on, The number of factors.

[0366] Standardized factors:

[0367]

[0368] Management criteria: (A higher value indicates a higher management intensity requirement)

[0369] Management level: (or I–V)

[0370] Confidence level: .

[0371] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0372] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0373] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0374] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0375] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0376] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for zoned management of groundwater source areas, characterized in that, include: S1: Acquire multi-source data of the study area, project them uniformly to the same coordinate system and map them to a unified spatial grid; S2: Construct a multi-factor system including human activity pressure factors, aquifer protection performance factors, groundwater quality comprehensive evaluation factors, protected area control intensity factors, and characteristic pollutant dynamic increment factors; S3: Calculate and construct protected area control intensity factors based on empirical formulas for protected area radii; S4: The improved DRASTIC model, which removes the topographic slope and increases the aquifer thickness, is used to calculate the aquifer protection performance factor; S5: The comprehensive evaluation factor of groundwater quality is calculated by combining the Fi score and the Nemerow composite index; S6: Calculate the human activity pressure factor based on land use type assignment; S7: Calculate the dynamic increment factor of characteristic pollutants by the difference in pollutant concentration under different operating conditions; S8: Standardize and robustly handle quantile truncation anomalies for all factors in the multi-factor system; S9: Integrate entropy weights and expert weights to construct an integrated weight that adapts to data completeness; S10: Basic management criteria based on multi-factor and adaptive weight calculation; S11: Use pollution risk results as a consistency constraint to perform risk correction on basic management criteria; S12: Use Monte Carlo simulation to propagate uncertainty and calculate the confidence level of each grid partition. S13: Set stability criteria based on confidence threshold, identify and extract partition verification areas; S14: Under budget constraints, with the goal of maximizing information gain, optimize the deployment of new monitoring points to achieve low-cost dynamic updates of zones.

2. The method according to claim 1, characterized in that, The formula for calculating the radius of the protected area in step S3 is: R=(α・K・I・T) / n, where α is the safety factor, K is the permeability coefficient, I is the hydraulic gradient, T is the migration time, and n is the effective porosity.

3. The method according to claim 1, characterized in that, The adaptive fusion weights in step S9 are: in, For the first The fusion weights of the factors; For the first The objective weights of each factor; For the first The management preference weights of each factor; This refers to the objective weighting ratio coefficient. Weighting of management preferences; For factor index; Adaptive data completeness: in, Effective monitoring information volume, expressed as the number of monitoring points, the number of effective samples, or the information coverage rate; Smoothing constant.

4. The method according to claim 1, characterized in that, The risk correction formula in step S11 is: in: For the first Corrected management criteria for each grid cell; For the first Basic management criteria for each grid unit; For the first Pollution risk value of each grid cell; To correct the strength coefficient; It is a monotonic mapping function; Number the grid cells.

5. The method according to claim 1, characterized in that, The rules for determining the review area in step S13 are as follows: Then the grid will be Included in the review process; in, For the first The partition management level of each grid cell obtained in this calculation; For the first The previous zoning management level of each grid unit; For the first Partition confidence of each grid cell; The confidence threshold; Number the grid cells.

6. The method according to claim 1, characterized in that, In step S14, with the goal of maximizing uncertainty reduction, new monitoring points are selected under budget constraints.

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